Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

Background Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens. Results Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib. Conclusions Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

Abrupt Dyskeratotic and Squamoid Cells in Poorly Differentiated Carcinoma: Case Study of Two Thoracic NUT Midline Carcinomas with Cytohistologic Correlation

Cytologic diagnosis of nuclear protein in testis (NUT) midline carcinoma (NMC) is important due to its aggressive behavior and miserable prognosis. Early diagnosis of NMC can facilitate proper management, and here we report two rare cases of thoracic NMC with cytohistologic correlation. In aspiration cytology, the tumor presented with mixed cohesive clusters and dispersed single cells, diffuse background necrosis and many neutrophils. Most of the tumor cells had scanty cytoplasm and medium-sized irregular nuclei, which had fine to granular nuclear chromatin. Interestingly, a few dyskeratotic cells or squamoid cell clusters were present in each case. Biopsy specimen histology revealed more frequent squamous differentiation, and additional immunohistochemistry tests showed nuclear expression of NUT. Because this tumor has a notorious progression and has been previously underestimated in terms of its prevalence, awareness of characteristic findings and proper ancillary tests should be considered in all suspicious cases

Clinicopathological Characteristics of Primary Pulmonary Hodgkin Lymphoma (PPHL): Two Institutional Experiences with Comprehensive Literature Review of 115 PPHL Cases

Primary pulmonary Hodgkin lymphoma (PPHL) is an extremely rare condition. Its clinicopathological characteristics remain unclear because of the limited number of patients with PPHL. The aim of this study was to comprehensively analyze the clinicopathological characteristics of PPHL. We reviewed the electronic medical records and pathology slides of our 10 PPHL patients. The female-to-male ratio was 6:4, and the mean age was 41 years. Although three patients had no symptoms, seven had localized or generalized symptoms, including cough, sputum, chest discomfort/pain, and weight loss. Some cases had not been diagnosed as PPHL in the initial needle biopsy. Four patients underwent surgical resection. With chemotherapy, eight patients achieved complete remission. We also conducted a thorough literature review on 105 previously reported PPHL cases. Among a total of 115 PPHL cases, the most common subtype was nodular sclerosis (37.4%). More than half of the cases (55%) were clinically suspected as infectious pneumonia. Of 61 patients whose biopsies were available, 27 (44.3%) were diagnosed correctly as Hodgkin lymphoma, whereas the misdiagnoses included tuberculosis, Langerhans cell histiocytosis, solitary fibrous tumor, and adenocarcinoma. We demonstrated that PPHL represents a diagnostic challenge on small biopsies. Recognizing that this rare tumor can mimic infectious and inflammatory diseases as well as malignancies is important because the accurate diagnosis of PPHL is essential for adequate clinical management

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer

Abstract Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693–0.805) in comparison to the pathologists’ scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01–1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment

Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

Background: Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens. Results: Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib. Conclusions: Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers.Y